Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,422
2	New Jersey	19,481
3	Massachusetts	15,765
4	Rhode Island	15,727
5	District of Columbia	14,520
6	Connecticut	12,987
7	Louisiana	12,121
8	Delaware	11,528
9	Illinois	11,294
10	Maryland	11,141
11	Arizona	10,161
12	Nebraska	9,770
13	Iowa	9,039
14	Mississippi	8,702
15	South Dakota	7,552
16	Virginia	7,232
17	Alabama	7,228
18	Michigan	7,013
19	Pennsylvania	7,013
20	Georgia	6,856
21	Indiana	6,843
22	Utah	6,598
23	Florida	6,568
24	Arkansas	6,567
25	South Carolina	6,471
26	Minnesota	6,309
27	North Carolina	5,928
28	Tennessee	5,849
29	New Mexico	5,631
30	Colorado	5,607
31	Nevada	5,579
32	California	5,490
33	Wisconsin	5,276
34	Texas	5,252
35	Kansas	4,811
36	North Dakota	4,598
37	Washington	4,378
38	Ohio	4,303
39	New Hampshire	4,226
40	Kentucky	3,464
41	Missouri	3,463
42	Oklahoma	3,271
43	Idaho	2,993
44	Wyoming	2,448
45	Maine	2,373
46	Puerto Rico	2,250
47	Oregon	1,979
48	Vermont	1,926
49	West Virginia	1,580
50	Alaska	1,448
51	Montana	807
52	Hawaii	623

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	488
2	Florida	419
3	South Carolina	278
4	Nevada	246
5	Louisiana	202
6	Arkansas	193
7	Texas	178
8	Utah	167
9	Georgia	157
10	Mississippi	154
11	Alabama	151
12	North Carolina	147
13	California	131
14	Iowa	120
15	Tennessee	109
16	New Mexico	98
17	Nebraska	95
18	Kansas	92
19	Idaho	89
20	Wisconsin	86
21	Delaware	84
22	Minnesota	84
23	Oklahoma	83
24	South Dakota	76
25	Ohio	75
26	Virginia	69
27	Indiana	63
28	Missouri	63
29	Oregon	61
30	Washington	58
31	Illinois	57
32	Maryland	53
33	Wyoming	52
34	Kentucky	51
35	Colorado	47
36	North Dakota	44
37	District of Columbia	42
38	Alaska	40
39	Massachusetts	40
40	Pennsylvania	40
41	New Jersey	37
42	New York	36
43	Puerto Rico	32
44	Maine	30
45	Michigan	29
46	Connecticut	28
47	New Hampshire	26
48	West Virginia	25
49	Montana	18
50	Hawaii	11
51	Rhode Island	6
52	Vermont	5

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,685
2	New York	1,600
3	Connecticut	1,210
4	Massachusetts	1,169
5	Rhode Island	875
6	District of Columbia	779
7	Louisiana	688
8	Michigan	616
9	Illinois	560
10	Maryland	524
11	Delaware	520
12	Pennsylvania	519
13	Indiana	389
14	Mississippi	349
15	Colorado	291
16	New Hampshire	269
17	Minnesota	258
18	Georgia	257
19	Ohio	240
20	New Mexico	234
21	Iowa	223
22	Arizona	218
23	Virginia	202
24	Alabama	187
25	Washington	172
26	Missouri	166
27	Nevada	162
28	Florida	159
29	California	150
30	Nebraska	140
31	South Carolina	139
32	Wisconsin	134
33	Kentucky	129
34	North Carolina	127
35	North Dakota	115
36	South Dakota	102
37	Oklahoma	97
38	Kansas	93
39	Vermont	89
40	Arkansas	87
41	Tennessee	84
42	Texas	83
43	Maine	77
44	Utah	52
45	West Virginia	51
46	Idaho	50
47	Oregon	48
48	Puerto Rico	47
49	Wyoming	34
50	Montana	20
51	Alaska	16
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	4
2	Massachusetts	4
3	District of Columbia	3
4	New Jersey	3
5	Arkansas	2
6	Louisiana	2
7	Maryland	2
8	Mississippi	2
9	New Hampshire	2
10	Rhode Island	2
11	Virginia	2
12	Alabama	1
13	California	1
14	Connecticut	1
15	Florida	1
16	Illinois	1
17	Indiana	1
18	Kansas	1
19	Minnesota	1
20	New Mexico	1
21	New York	1
22	North Carolina	1
23	Pennsylvania	1
24	South Carolina	1
25	South Dakota	1
26	Alaska	0
27	Colorado	0
28	Delaware	0
29	Georgia	0
30	Hawaii	0
31	Idaho	0
32	Iowa	0
33	Kentucky	0
34	Maine	0
35	Michigan	0
36	Missouri	0
37	Montana	0
38	Nebraska	0
39	Nevada	0
40	North Dakota	0
41	Ohio	0
42	Oklahoma	0
43	Oregon	0
44	Puerto Rico	0
45	Tennessee	0
46	Texas	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	130,805	1	99
Lake	Tennessee	98,489	2	99
Dakota	Nebraska	88,085	3	99
Buena Vista	Iowa	86,239	4	99
Lincoln	Arkansas	79,622	5	99
Davidson	Tennessee	13,467	207	93
Richland	South Carolina	7,901	509	83
Orange	California	4,078	1058	66
York	South Carolina	3,897	1106	64
Pierce	Washington	3,116	1325	57

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,165	3	99
Early	Georgia	3,042	4	99
Essex	New Jersey	2,529	5	99
Richland	South Carolina	195	678	78
Davidson	Tennessee	158	774	75
Pierce	Washington	107	997	68
Orange	California	103	1020	67
York	South Carolina	43	1513	51

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.